Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2024
Abstract
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive strategies to learn and predict these tailored masks, potentially enhancing performance while significantly reducing computational costs during training. To verify our PIP designs, we conduct extensive experiments on the highly challenging Traveling Salesman Problem with Time Window (TSPTW), and TSP with Draft Limit (TSPDL) variants under different constraint hardness levels. Notably, our PIP is generic to boost many neural methods, and exhibits both a significant reduction in infeasible rate and a substantial improvement in solution quality.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Sustainability
Publication
Proceedings of the 38th Conference on Neural Information Processing (NeurIPS 2024), Vancouver, Canada, December 10-15
First Page
1
Last Page
25
Publisher
Neural information Processing Systems Foundation
City or Country
California
Citation
BI, Jieyi; MA, Yining; ZHOU, Jianan; SONG, Wen; CAO, Zhiguang; WU, Yaoxin; and ZHANG, Jie.
Learning to handle complex constraints for vehicle routing problems. (2024). Proceedings of the 38th Conference on Neural Information Processing (NeurIPS 2024), Vancouver, Canada, December 10-15. 1-25.
Available at: https://ink.library.smu.edu.sg/sis_research/9814
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://neurips.cc/virtual/2024/poster/95638